Computational prediction of hot-electron chemistry: Towards electronic control of catalysis
Lead Research Organisation:
University of Warwick
Department Name: Chemistry
Abstract
Higher living standards and a growing world population are the drivers behind continuous increases in greenhouse gas emission and industrial energy use. This provides growing pressure on chemical industries to develop more sustainable and efficient chemical transformations based on innovative new technologies. Light-driven plasmonic catalysis offers a promising route to more sustainable and energy efficient chemical transformations than conventional industrial-scale catalysis by replacing petrochemical reactants and energy sources with abundant feedstocks such as carbon dioxide from the atmosphere and renewable energy from sunlight. In addition, light energy can selectively be transferred via excited electrons in metal nanoparticles, so-called "hot" electrons, to molecules and enables more specific chemical reactions than conventional catalysis, potentially increasing yield and decreasing unwanted side products.
Underlying this unconventional form of chemistry is the intricate coupling of light, hot electrons, and reactant molecules, the lack of understanding of which has inhibited systematic design and study of reaction parameters such as particle size, shape, and optimal light exposure. A predictive theory of hot-electron chemistry will support the adaptation of this technology in the chemical industry, which holds the potential to significantly reduce the industry's carbon footprint.
The aim of this project is to develop and exploit a computational simulation framework to understand, predict, and design light-driven chemical reactions on light-sensitive metallic nanoparticles and surfaces, so-called plasmonic nanocatalysts. The vision behind this fellowship is to provide quantum theoretical methods that fill a conceptual and methodological gap by providing accurate and feasible computational prediction of experimentally measurable chemical reaction rates as a function of catalyst design parameters relevant to the real-world application of this technology.
In synergy with experimental project partners, the fellow will lead a research team of 2 postdoctoral researchers to develop highly efficient computational chemistry methodology, which will be applied to scrutinize mechanistic proposals, support and guide experimental efforts on light-driven plasmonic carbon dioxide reduction chemistry, and to construct reaction rate models relevant to improve the industrial viability of this technology. The aim is to provide a step-change in the mechanistic understanding of light-driven plasmonic reduction catalysis on the example of carbon monoxide and carbon dioxide transformation to enable rational design of catalyst materials with wide implications for continuous photochemistry and electrochemistry applications in industry. These applications will be explored by continuous engagement efforts of the fellow with leading chemical and petrochemical companies. With this project, the fellow will establish an international track record by fostering existing and establishing new collaborations with the goal to become a recognized researcher in this comparably young field.
Underlying this unconventional form of chemistry is the intricate coupling of light, hot electrons, and reactant molecules, the lack of understanding of which has inhibited systematic design and study of reaction parameters such as particle size, shape, and optimal light exposure. A predictive theory of hot-electron chemistry will support the adaptation of this technology in the chemical industry, which holds the potential to significantly reduce the industry's carbon footprint.
The aim of this project is to develop and exploit a computational simulation framework to understand, predict, and design light-driven chemical reactions on light-sensitive metallic nanoparticles and surfaces, so-called plasmonic nanocatalysts. The vision behind this fellowship is to provide quantum theoretical methods that fill a conceptual and methodological gap by providing accurate and feasible computational prediction of experimentally measurable chemical reaction rates as a function of catalyst design parameters relevant to the real-world application of this technology.
In synergy with experimental project partners, the fellow will lead a research team of 2 postdoctoral researchers to develop highly efficient computational chemistry methodology, which will be applied to scrutinize mechanistic proposals, support and guide experimental efforts on light-driven plasmonic carbon dioxide reduction chemistry, and to construct reaction rate models relevant to improve the industrial viability of this technology. The aim is to provide a step-change in the mechanistic understanding of light-driven plasmonic reduction catalysis on the example of carbon monoxide and carbon dioxide transformation to enable rational design of catalyst materials with wide implications for continuous photochemistry and electrochemistry applications in industry. These applications will be explored by continuous engagement efforts of the fellow with leading chemical and petrochemical companies. With this project, the fellow will establish an international track record by fostering existing and establishing new collaborations with the goal to become a recognized researcher in this comparably young field.
Planned Impact
Continuing fossil fuel depletion and accumulation of greenhouse gases in the atmosphere will become the defining threats to living standards, a healthy society, and energy security for more than 10 billion people inhabiting the planet in the second half of the 21st century. To significantly reduce these threats, while maintaining the important socioeconomic role of industrial catalysis, innovative catalytic pathways need to be identified that are both efficient and environmentally sustainable. This extraordinary challenge amounts to nothing less than transforming the national and global petrochemicals and commodity chemicals industry to build on abundant feedstocks such as carbon dioxide, water, and renewable energy from sunlight with product yields that push beyond the thermodynamic limitations of conventional industrial catalysis. Our proposed research on computationally predicting light-enhancement of catalysis will directly impact the realization of a technology that can contribute to such a transformation.
1. Academic Impact
Hot-electron chemistry, the subject of this project, touches on many disciplines and is driven by plasmonic light-matter interaction with numerous proposed applications beyond catalysis that include sensors, mobile fuel generation devices, and energy harvesting materials. These areas are of specific interest for defence applications developed by academics, the UK Defence Science and Technology Laboratory, and the US Department of Defense Research Offices. The methods we develop and the models we will construct will impact our Academic Beneficiaries and the above stated research areas via a dissemination strategy targeted to actively engage a wide range of scientific communities.
2. Economic and Environmental Impact via Innovation in Industrial Catalysis
This project will develop models to predict catalyst structure, cost, and efficiency relations that support the adaptation of hot-electron-enhanced chemistry in industrial catalysis. This has the potential to provide drastic gains in energy efficiency, product selectivity, and a reduction in carbon footprint on a planet-wide scale. This will provide an important know-how advantage for the UK petrochemical and commodity chemicals industry represented by companies such as BP and Johnson-Matthey against low-tech competitors in emerging markets. The Fellow is new to the UK research and innovation landscape and this fellowship will enable him to identify industrial contacts and actively engage with them to effectively communicate the commercial implications of our findings.
3. Societal Impact via Maintained and Improved Living Standards
Long-term industrial adaptation of plasmonic catalysis technology will benefit environmental protection and energy efficiency on a scale that will affect individual UK consumers and households. The research we propose aims to impact this technology by providing theoretical rate models and structure-function relationships that support catalyst design. We propose several outreach efforts to inform policy makers and the wider public of the importance and potential benefit of innovation in industrial catalysis and the important role of computational modelling as a driver for such innovation.
4. Impact on People and Skills
We will use and develop methods of computational science, artificial intelligence, and data-driven design, which are key to further develop the UK's digital economy. The two postdoctoral researchers funded by this project will develop skills that are in high demand in academia and industry. They have the potential to become leading innovators in the UK's growing data science and computational modelling sector. The fellow will fully utilize this fellowship to establish himself as an internationally recognized leader in the field of plasmonic catalysis and computational chemistry. This will be supported by our dissemination plans designed to engage a diverse range of academics and industrial stakeholders
1. Academic Impact
Hot-electron chemistry, the subject of this project, touches on many disciplines and is driven by plasmonic light-matter interaction with numerous proposed applications beyond catalysis that include sensors, mobile fuel generation devices, and energy harvesting materials. These areas are of specific interest for defence applications developed by academics, the UK Defence Science and Technology Laboratory, and the US Department of Defense Research Offices. The methods we develop and the models we will construct will impact our Academic Beneficiaries and the above stated research areas via a dissemination strategy targeted to actively engage a wide range of scientific communities.
2. Economic and Environmental Impact via Innovation in Industrial Catalysis
This project will develop models to predict catalyst structure, cost, and efficiency relations that support the adaptation of hot-electron-enhanced chemistry in industrial catalysis. This has the potential to provide drastic gains in energy efficiency, product selectivity, and a reduction in carbon footprint on a planet-wide scale. This will provide an important know-how advantage for the UK petrochemical and commodity chemicals industry represented by companies such as BP and Johnson-Matthey against low-tech competitors in emerging markets. The Fellow is new to the UK research and innovation landscape and this fellowship will enable him to identify industrial contacts and actively engage with them to effectively communicate the commercial implications of our findings.
3. Societal Impact via Maintained and Improved Living Standards
Long-term industrial adaptation of plasmonic catalysis technology will benefit environmental protection and energy efficiency on a scale that will affect individual UK consumers and households. The research we propose aims to impact this technology by providing theoretical rate models and structure-function relationships that support catalyst design. We propose several outreach efforts to inform policy makers and the wider public of the importance and potential benefit of innovation in industrial catalysis and the important role of computational modelling as a driver for such innovation.
4. Impact on People and Skills
We will use and develop methods of computational science, artificial intelligence, and data-driven design, which are key to further develop the UK's digital economy. The two postdoctoral researchers funded by this project will develop skills that are in high demand in academia and industry. They have the potential to become leading innovators in the UK's growing data science and computational modelling sector. The fellow will fully utilize this fellowship to establish himself as an internationally recognized leader in the field of plasmonic catalysis and computational chemistry. This will be supported by our dissemination plans designed to engage a diverse range of academics and industrial stakeholders
Organisations
- University of Warwick (Fellow, Lead Research Organisation)
- Technical University Berlin (Collaboration)
- Philipp University of Marburg (Collaboration)
- Forschungszentrum Jülich (Collaboration)
- DIAMOND LIGHT SOURCE (Collaboration)
- University of Science and Technology of China USTC (Collaboration)
- Max Planck Institutes (Project Partner)
- Technical University of Berlin (Project Partner)
- University of Illinois Urbana-Champaign (Project Partner)
- University of Science and Technology of China (Project Partner)
Publications
Blowey P
(2020)
Alkali Doping Leads to Charge-Transfer Salt Formation in a Two-Dimensional Metal-Organic Framework
in ACS Nano
Chaudhuri S
(2022)
Coexistence of carbonyl and ether groups on oxygen-terminated (110)-oriented diamond surfaces
in Communications Materials
Douglas-Gallardo OA
(2021)
Plasmonic enhancement of molecular hydrogen dissociation on metallic magnesium nanoclusters.
in Nanoscale
Gardner J
(2023)
Efficient implementation and performance analysis of the independent electron surface hopping method for dynamics at metal surfaces.
in The Journal of chemical physics
Gardner J
(2022)
NQCDynamics.jl: A Julia package for nonadiabatic quantum classical molecular dynamics in the condensed phase.
in The Journal of chemical physics
Gastegger M
(2020)
A deep neural network for molecular wave functions in quasi-atomic minimal basis representation.
in The Journal of chemical physics
Hall SJ
(2023)
Characterizing Molecule-Metal Surface Chemistry with Ab Initio Simulation of X-ray Absorption and Photoemission Spectra.
in The journal of physical chemistry. C, Nanomaterials and interfaces
Hofmann O
(2021)
First-principles calculations of hybrid inorganic-organic interfaces: from state-of-the-art to best practice
in Physical Chemistry Chemical Physics
Klein B
(2019)
Molecule-Metal Bond of Alternant versus Nonalternant Aromatic Systems on Coinage Metal Surfaces: Naphthalene versus Azulene on Ag(111) and Cu(111)
in The Journal of Physical Chemistry C
Klein B
(2021)
The nuts and bolts of core-hole constrained ab initio simulation for K-shell x-ray photoemission and absorption spectra
in Journal of Physics: Condensed Matter
Klein B
(2022)
Using polycyclic aromatic hydrocarbons for graphene growth on Cu(111) under ultra-high vacuum
in Applied Physics Letters
Klein B
(2020)
Enhanced Bonding of Pentagon-Heptagon Defects in Graphene to Metal Surfaces: Insights from the Adsorption of Azulene and Naphthalene to Pt(111)
in Chemistry of Materials
Klein B
(2021)
Topology Effects in Molecular Organic Electronic Materials: Pyrene and Azupyrene**
in ChemPhysChem
Klein BP
(2022)
Topological Stone-Wales Defects Enhance Bonding and Electronic Coupling at the Graphene/Metal Interface.
in ACS nano
Knol M
(2021)
The stabilization potential of a standing molecule.
in Science advances
Kulik H
(2022)
Roadmap on Machine learning in electronic structure
in Electronic Structure
Litman Y
(2022)
Dissipative tunneling rates through the incorporation of first-principles electronic friction in instanton rate theory. II. Benchmarks and applications.
in The Journal of chemical physics
Litman Y
(2022)
Dissipative tunneling rates through the incorporation of first-principles electronic friction in instanton rate theory. I. Theory.
in The Journal of chemical physics
Liu DQ
(2021)
Adiabatic versus non-adiabatic electron transfer at 2D electrode materials.
in Nature communications
Mousley PJ
(2022)
Direct Experimental Evidence for Substrate Adatom Incorporation into a Molecular Overlayer.
in The journal of physical chemistry. C, Nanomaterials and interfaces
Ryan P
(2022)
Thermodynamic Driving Forces for Substrate Atom Extraction by Adsorption of Strong Electron Acceptor Molecules.
in The journal of physical chemistry. C, Nanomaterials and interfaces
Schütt KT
(2019)
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.
in Nature communications
| Description | The aim of this project is to develop a quantum dynamical theory of hot-electron chemistry that can reliably predict light-driven reaction outcomes for hydrogen evolution and carbon dioxide reduction on plasmonic nanocatalysts. The main efforts in this project involve (A) The creation of machine-learning-based models of electronic structure to efficiently simulate chemical dynamics at metal surfaces, (B) computational method development of new nonadiabatic dynamics methods incl. software development, (C) simulation of key quantities to predict reaction outcomes in hot-electron catalysis and to design new catalyst materials. In the first year of this award, the research team has been assembled and important preliminary method developments (B) have started. Initial progress in (A) and (C) have led to scientific publications (either published or currently being drafted). The main outcome of year 1 was the creation of a machine learning algorithm that can predict molecular wave functions (A), which will be highly useful for a broad range of applications in computational simulation and for the next steps in this project. [Nature Commun., 5024 (2019) and J. Chem. Phys. 153, 044123 (2020) ] At the end of year two of the project, we have been able to further develop our ML methodology to predict nonadiabatic coupling via electronic friction tensors. These tensors are important input quantities to describe how excited electrons in metals (created by light or non-thermal effects) interact with molecular reactants at the catalyst surface. [J. Phys. Chem. C 124, 1, 186-195 (2020)]. We have furthermore completed an important milestone, namely, we have established the predictive power of an electronic friction-based description of indirect hot-electron chemistry against benchmark experiments for the case of nitrous oxide reaction at a Au(111) surface. Our work establishes which aspects of non-thermal surface chemistry can be described with existing formulations of electronic friction and which ones cannot. This is an important finding that can potentially inspire new experiments and will hopefully incite the development of new and improved methods. [JACS Au 1, 2, 164-173 (2021)]. At the end of year three (March 2022), we have made further progress on objective (A) in developing machine learning models of electronic structure for condensed phase systems [npj Comp. Mater. Sci. 8 158 (2022)], which we will soon be able to apply for the simulation of reactive chemistry at surfaces. The preliminary work conducted as part of this project helped to develop a Starting Grant funding application to the European Research Council to develop machine learning models of electronic structure. This five year project was funded and will run alongside this fellowship from June 2022 onwards. This will allow us to focus further on dynamics and on addressing application problems in hot electron catalysis as part of this project. Regarding the development of new nonadiabatic dynamics methods (B), we have released a comprehensive software package called (NCQDynamics.jl) that will form the platform of our future investigations in the area of nonadiabatic and light-driven dynamics in condensed phase [J. Chem. Phys 156, 174801 (2022)]. The software features a variety of different dynamics methods which we plan to expand in the coming year. Further progress on the simulation of hot electron catalysis (C) has been made. We have been able to characterize the influence of light on creating hot electron distributions in conventional and emerging plasmonic materials (Silver and Aluminium nanoparticles) and have studied the potential for plasmonic enhancement of molecular hydrogen dissociation on metallic magnesium nanoclusters [Nanoscale 13, 11058-11068 (2021)]. Our results suggest that the mechanism for hot electron effects in hydrogen dissociation is different than the mechanism of how hot electrons affect hydrogen evolution (dissociative recombination). As a consequence, the strategies to optimise catalysts for one or the other will likely need to be different. At the end of year four (March 2023), we have made significant advances in all objectives. Regarding objective (B), we have implemented an improved version of the independent electron surface hopping method able to describe nonadiabatic dynamics that arise from direct metal-to-molecule electron transfer. We have published a study where we systematically assess the performance and limitations of this method in describing thermally-assisted desorption and scattering events for well-defined model systems. This has established a better picture of the applicability of this method for the study of hot electron dynamics [J. Chem. Phys. 158, 064101 (2023)]. We have furthermore progressed the understanding of how nonadiabatic hot electron effects contribute to the stereodynamics of molecular scattering events at metal surfaces [Phys. Chem. Chem. Phys. 24, 19753-19760 (2022)]. We have also developed an improved machine learning interatomic potential to describe long-range interactions between molecules and metal surfaces. [Digital Discovery 1, 463-475 (2022)] This potential will enable the efficient structure prediction of large-scale models of molecules adsorbed at metal surfaces and nanoparticles. |
| Exploitation Route | Research outcomes in the three activity streams of this project are geared to generate outcomes that can be widely used and easily taken forward by others. In particular, the developed machine-learning algorithms to predict molecular wave functions (SchNOrb, ACEhamiltonians.jl) have been made public and have the potential to be useful for a wide range of researchers in academia and industry. This will include their application and repurposing to address research problems in synthetic chemistry and catalysis. We have also released a general-purpose software package to perform nonadiabatic dynamics simulations in condensed phase (NQCDynamics.jl) that will likely be widely used to study photochemical dynamics. Our findings on the limitations of nonadiabatic dynamics methods will provide crucial input to other experimental and theoretical researchers to improve simulation capabilities of photocatalytic processes in the future. |
| Sectors | Chemicals,Energy,Environment |
| Description | ARCHER2 eCSE-4 call: Relativistic all-electron orbital-constrained Density Functional Theory to simulate xray photoemission and absorption spectroscopy |
| Amount | £82,784 (GBP) |
| Funding ID | ARCHER2-eCSE04-3 |
| Organisation | ARCHER |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 03/2022 |
| End | 12/2022 |
| Description | Achieving the sustainability and scalability of numeric-atomic-orbital-based linear response and electron-phonon functionality in FHI-aims |
| Amount | £81,484 (GBP) |
| Funding ID | ARCHER2-eCSE01-16 |
| Organisation | ARCHER |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 11/2020 |
| End | 10/2021 |
| Description | Artificial and Augmented Intelligence for Automated Scientific Discovery |
| Amount | £1,014,318 (GBP) |
| Funding ID | EP/S000356/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2018 |
| End | 06/2021 |
| Description | Deep-learning-enhanced simulation of plasmonic CO2 catalysis |
| Amount | € 90,800 (EUR) |
| Funding ID | J4522-N |
| Organisation | Austrian Science Fund (FWF) |
| Sector | Academic/University |
| Country | Austria |
| Start | 01/2021 |
| End | 12/2023 |
| Description | ERC Starting Grant DeepSpark |
| Amount | € 1,491,992 (EUR) |
| Funding ID | 101039492 |
| Organisation | European Research Council (ERC) |
| Sector | Public |
| Country | Belgium |
| Start | 06/2022 |
| End | 05/2027 |
| Description | MSCA postdoctoral fellowship, Atomic-scale design of superlubricity of carbon nanostructures on metallic substrates |
| Amount | £318,595 (GBP) |
| Funding ID | 101103630 |
| Organisation | Marie Sklodowska-Curie Actions |
| Sector | Charity/Non Profit |
| Country | Global |
| Start | 09/2023 |
| End | 08/2025 |
| Title | Au@C for SchNet+vdW |
| Description | Gold nanoparticle data on diamond(110) surfaces computed with Density Functional Theory based on ref. https://arxiv.org/pdf/2202.13009.pdf |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | The data enables the development of long-range machine learning potentials to advance the field of material science. |
| Title | Determining the effect of hot electron dissipation on molecular scat- tering experiments at metal surfaces: Figure data |
| Description | Each file contains all model datapoints for each named figure. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://figshare.com/articles/dataset/Determining_the_effect_of_hot_electron_dissipation_on_molecula... |
| Title | Determining the effect of hot electron dissipation on molecular scattering experiments at metal surfaces: Figure data |
| Description | Each file contains all model datapoints for each named figure. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://figshare.com/articles/dataset/Determining_the_effect_of_hot_electron_dissipation_on_molecula... |
| Title | G-SchNet for OE62 |
| Description | Minimal tutorial for using the SchNet+H models and the adapted G-SchNet model individually and for creating new molecules with targeted properties. Includes the code from https://github.com/rhyan10/G-SchNetOE62 and https://github.com/schnarc/SchNarc/ used for this tutorial. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | future model development, reproducibility, learning of developed methods |
| URL | https://figshare.com/articles/dataset/G-SchNet_for_OE62/20146943 |
| Title | SchNOrb machine learning model |
| Description | The SchNOrb machine learning model is a deep tensor neural network that reads in molecular geometries (atom positions and elemental composition) and predicts molecular wave functions and other molecular electronic properties (such as the total energy). |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Impact | The SchNOrb model is open source and publicly available and can be used to generate a machine-learning based surrogate models of the electronic structure of a molecule. This model can be extended in many different ways to benefit research in molecule and materials design with applications in pharmaceutical and catalysis research. |
| URL | https://github.com/atomistic-machine-learning/SchNOrb |
| Description | David Duncan, I09, Diamond Light Source |
| Organisation | Diamond Light Source |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | I have contributed computational simulation expertise to research projects by Dr David Duncan and Dr. Tien-Lin Lee at Diamond Light Source. Team members from my research group will provide simulation data to support the design and characterization of novel metal catalysts. |
| Collaborator Contribution | Diamond Light Source (via direct collaboration with Dr David Duncan and Dr. Tien-Lin Lee) have contributed funding to support 50% of the cost of a PhD studentship (£51,184) and funding for 3 months of salary for a postdoctoral fellow (£11,681) to support my research efforts in the wider context of this project. The corresponding staff members will perform experimental measurements at Diamond Light Source which will support the efforts in this award. |
| Impact | No outcomes have yet resulted from this collaboration. |
| Start Year | 2019 |
| Description | Dr. Christian Wagner, Research Centre Julich (Germany) |
| Organisation | Julich Research Centre |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | As part of this longstanding and ongoing collaboration, we have provided supporting computational simulations of the structure, stability and dynamics of surface nanostructures relevant for nanotechnology applications and catalysis. Our contribution has helped to understand atom-resolved experimental measurements of surface electrostatic potentials and of surface dynamics of metastable structures. |
| Collaborator Contribution | The collaboration partners are conducting scanning probe microscopy experiments of surface nanostructures to understand the atomic scale fabrication and manipulation of atoms and single molecules. This is relevant in the context of nanotechnology and catalysis and these experiments provide important benchmark data to improve the simulation methods that are being developed as part of this fellowship. |
| Impact | One high impact publication has so far resulted from this collaboration: DOI: 10.1126/sciadv.abj9751 |
| Start Year | 2019 |
| Description | Jiang, USTC, Hefei |
| Organisation | University of Science and Technology of China USTC |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Within this research collaboration, we provide expertise on nonadiabatic electron-nuclear coupling effects during gas-surface dynamics in heterogeneous catalysis simulations. We have provided the collaborators with a large amount of simulation data for several relevant systems incl. molecular hydrogen dissociation on Ag(111) surfaces and nitrous oxide scattering on Au(111). |
| Collaborator Contribution | The collaboration partners have provided us with their expertise in machine learning-based potential energy surface interpolation and have given us access to their extensive software stack for gas-surface dynamics simulations. |
| Impact | Two publications have so far resulted from this collaboration: DOI: 10.1021/acs.jpcc.9b09965 DOI: 10.1021/jacsau.0c00066 This collaboration is not multi-disciplinary |
| Start Year | 2019 |
| Description | Klaus-Robert Mueller, TU Berlin |
| Organisation | Technical University Berlin |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | I have contributed my expertise in electronic structure theory and computational chemistry to this collaboration. I and my team members have performed a large nuber of quantum chemical calculations to generate training data sets. These data sets are used to train deep learning models. |
| Collaborator Contribution | The collaborator and his team have provided us with crucial expertise and software to develop deep machine learning models that support the efforts in this award. The contribution consists of time for regular discussions, joint manuscript preparation, and hosting members of my team at the TU Berlin. |
| Impact | Two publications has resulted from this collaboration: DOI: 10.1038/s41467-019-12875-2 |
| Start Year | 2019 |
| Description | Prof. Michael Gottfried, Department of Chemistry, University of Marburg |
| Organisation | Philipp University of Marburg |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | In this research collaboration, my research group contributes expertise on computational simulation of x-ray absorption (XAS) and x-ray photoemission spectroscopy (XPS) towards a joint experimental/computational characterization of complex hybrid metal-organic interfaces with relevance in the context of organic electronics devices such as organic light-emitting diodes, and photovoltaic devices. Experimental XAS and XPS spectra of device-relevant organic molecules are typically very difficult to interpret and atomistic simulation is a key technique to facilitate this interpretation. |
| Collaborator Contribution | The experimental partner (Prof. Gottfried, U Marburg) is an expert in the experimental characterization of metal-organic interfaces with a special focus on topological design of molecules and interfaces for organic electronics applications. This work crucially depends on x-ray spectroscopical characterization, which typically involves data acquisition at synchrotron sources such as Diamond Light Source (UK). The partners contribute their experimental measurements and expertise on this subject. |
| Impact | The collaboration has yielded 4 impactful publications in total on topological effects in metal-organic interfaces. Molecular Topology and the Surface Chemical Bond: Alternant Versus Nonalternant Aromatic Systems as Functional Structural Elements, DOI: 10.1103/PhysRevX.9.011030 Enhanced Bonding of Pentagon-Heptagon Defects in Graphene to Metal Surfaces: Insights from the Adsorption of Azulene and Naphthalene to Pt(111), DOI:10.1021/acs.chemmater.9b03744 Molecule-Metal Bond of Alternant versus Nonalternant Aromatic Systems on Coinage Metal Surfaces: Naphthalene versus Azulene on Ag(111) and Cu(111), DOI: 10.1021/acs.jpcc.9b08824 Topology Effects in Molecular Organic Electronic Materials: Pyrene and Azupyrene, DOI: 10.1002/cphc.202100222 |
| Start Year | 2017 |
| Title | NQCDynamics.jl |
| Description | NQCDynamics.jl package provides a simulation framework for established and emerging semiclassical and mixed quantum-classical molecular dynamics methods for atomistic condensed phase systems. The code provides several interfaces to existing atomistic simulation frameworks, electronic structure codes, and machine learning representations. In addition to the existing methods, the package provides infrastructure for developing and deploying new dynamics methods which aim to benefit reproducibility and code sharing in the field of condensed phase quantum dynamics. Examples of implemented simulation methods include classical molecular dynamics, Langevin dynamics, fewest-switches surface hopping, ring polymer molecular dynamics, Ehrenfest dynamics, nonadiabatic ring polymer molecular dynamics, and many more. |
| Type Of Technology | Software |
| Year Produced | 2022 |
| Open Source License? | Yes |
| Impact | The software represents the first implementation of semiclassical and mixed quantum-classical molecular dynamics in the Julia programming language and has been well received after its announcement on Social Media. The public code development hosted on the GitHub platform is actively being followed by the community and first expressions of interest exist in contributing to extending the software functionality. |
| URL | https://nqcd.github.io/NQCDynamics.jl/dev/ |
| Title | SchNet+vdW |
| Description | Code to add long-range corrections to short-ranged (machine learning) potentials. |
| Type Of Technology | Software |
| Year Produced | 2022 |
| Open Source License? | Yes |
| Impact | Is expected to be used by many researchers. |
| URL | https://github.com/maurergroup/SchNet-vdW |
| Description | AI3SD Interview |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | As part of an AI3SD funded project that is directly related to my UKRI FLF project, I was interviewed about the main outcomes of the project and the background and potential future impact of our findings. The interview was published in writing by the EPSRC-FUNDED AI3SD network and the University of Southampton. The main goal of the interview was to make our results more digestible for a wider audience of non-specialists and practitioners. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://eprints.soton.ac.uk/443553/1/AI3SD_Interview_Series_Interview_5_RM.pdf |
| Description | Co-Organization of the DQML22 workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | The DQML 2022 conference is organized by the University of Luxembourg (Prof. Alexandre Tkatchenko and Dr. Mario Galante) and the University of Warwick (Assoc.-Prof. Dr. Reinhard Maurer and Dr. Julia Westermayr). The meeting will be held in Hintertux, Austria at the Alpenbad Hotel Hohenhaus from 1st of February to the 4th of February 2022. This conference brings together researchers from molecular dynamics, quantum effects, and machine learning in materials science and computational chemistry. 4 invited speakers have confirmed their participation: Dr. Carla Verdi, Prof. Rocco Martinazzo, Dr. Oliver Unke, and Prof. Allesandro Lunghi. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://dqml22.github.io/ |
| Description | Coorganization fo CECAM ultrafast plasmonics workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | The Psi-K & CECAM sponsored meeting "Light-matter interaction and ultrafast nonequilibrium dynamics in plasmonic materials" was held from 18th to 21st of July 2022 at the University of Warwick. It featured 28 talks, 4 discussion sessions, and 10 posters. It was attended by 42 in-person attendees from 12 different countries and broadcast as a webinar with between 3 and 17 virtual attendees at any time. A full theoretical description of light-matter interaction and plasmon-induced ultrafast non-equilibrium dynamics is a formidable challenge that demands an intrinsically multidisciplinary and multiscale approach. A variety of different approaches based on time-dependent Density Functional Theory, many-body perturbation theory, molecular dynamics, Mie theory, continuum electrodynamics, and combinations thereof have emerged in recent years to address many of the open questions in plasmonics. Further improvements in theoretical descriptions are crucial to optimize SPP generation and amplification in materials, to tailor losses and plasmonic lifetimes, as well as to integrate plasmonic effects into semiconductor technology to create new quantum materials. Due to the diverse aspects of this problem, a coherent research community around theoretical plasmonics is only slowly emerging. The aim of this workshop was to assess the state of computational methods in this field, to identify major challenges, as well as to provide engagement between disparate communities to create space for cross-community collaboration. The workshop brought attention to the diversity of materials, physical phenomena, and theoretical and experimental methods that are relevant for plasmonics and for its application to the field of photocatalysis. Several presenters emphasised the opportunities to discover new ultrafast physics when strong plasmonic resonances at interfaces are triggered such as optical nonlinearity and plasmon-induced photoemission. An emerging research stream is the use of polarised light and chiral structures to engineer unique materials properties. Exciting new experimental techniques are emerging and their capability to harness strong near-field effects was among the discussion topics of this workshop. These include tip-enhanced Raman spectroscopy, EELS-STEM, as well as photon-induced near-field electron microscopy. Theoretical investigations need to be able to predict the non-linear response that these measurements observe. The common understanding of plasmon-assisted chemical reactions typically relies on the energy exchange among hot-electrons (photo or plasmonically excited on plasmonic nanoparticles) and molecular adsorbates. An important issue that still remains to be addressed in this field consists in revealing the influence of more complex many-body excitations and quasiparticles for chemical reactions in plasmonic compounds. The question of whether plasmons directly couple to phonons or only act a source of hot electrons still remains to be addressed. A clear need was identified for electronic structure techniques that are scalable and able to address strong correlation effects in materials as well as strong light-matter interaction. Currently few viable methods exist to study electronic excited states at molecule-metal interfaces, which also directly limits the use of nonadiabatic molecular dynamics techniques and real-time time-dependent DFT to study how electronic excitation drives dynamics at the interface. Promising methods were identified as exchange-correlation functionals that correctly predict the level alignment at the interface and many body perturbation theory methods that capture frequency and lifetime of 1-particle and 2-particle excitations. The special properties of plasmonic and other collective resonances in unusual plasmonic materials such as Magnesium or two-dimensional materials was also discussed. In the latter case, it was raised that this likely requires electronic structure methods that can address strong correlation effects as they occur in the presence of flat bands (e.g. the constrained RPA method). It was discussed that observed dynamical mechanisms likely differ between pulsed lasers and continuous white light illumination. It remains unclear if dynamics at the interface will be dominated by thermalised electrons at elevated temperatures or truly non-equilibrium electron distributions created by light excitation. Collective plasmonic excitations mainly decay into electronic excitations at the Fermi level. The origin of this effect is currently being investigated, but a full theoretical understanding is currently lacking. However, real-time TD-DFT simulations of plasmon decay also show evidence of this effect. This is an example where two different communities found corroborating evidence of the same effect, which we established as part of this conference. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.cecam.org/workshop-details/1164 |
| Description | NaturePortfolio Blogpost |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | A blog post on Nature Chemistry Community that describes the results of a high impact publication in Nature Communications to a lay audience. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://chemistrycommunity.nature.com/posts/adiabatic-versus-non-adiabatic-electron-transfer-at-2d-e... |
| Description | RSC request for expert opinion on machine learning in chemistry |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | I was asked by the Royal Society of Chemistry to provide my expert opinion on the future of artificial intelligence and machine learning in the field of chemistry and what the RSC and the Faraday Division in particular can do in the future to provide more visibility to this field. |
| Year(s) Of Engagement Activity | 2021 |
| Description | TheNewStack media interview |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | TheNewStack is a blog and news webpage for developers and engineers building and managing new software stacks around the world that are built on open source technologies and distributed infrastructures. A reporter interviewed me about the results and impact of our recent publication in Nature Communications [Nature Communications10, 5024 (2019)]. This has led to a blog post that was published on the subject that showed high engagement on social media. The article brought the subject of machine learning methodology in quantum chemistry closer to the wider computer science and software development community. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://thenewstack.io/deep-physics-ai-helps-predict-quantum-molecular-wave-functions/ |
| Description | provided expert opinion on the value of EU funding to UK Science Minister George Freeman MP |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Policymakers/politicians |
| Results and Impact | I was asked by George Freeman MP (UK Minister of Science) to attend a small panel to discuss the value of EU funding to the UK and to provide my opinion on the importance of funding and potential opportunities to replicate opportunities in the event that the UK will not associate to Horizon Europe program. |
| Year(s) Of Engagement Activity | 2022 |